Comparing Landfalling Tropical Cyclones Objectively Tracked in High-Resolution Global Climate Models to Synthetic Tracks Generated Using Statistical-Dynamical Downscaling Methods

We compare the simulation of landfalling tropical cyclones (TCs) using two widely-used tools for studying TC climatology: high-resolution climate models (which directly simulate TCs that can be tracked in model output) and statistical-dynamical downscaling (SDD) models (which generate synthetic storms based on a model's large-scale climatology). We analyze data from the High-Resolution Model Intercomparison Project (HighResMIP). We compare objectively tracked global climate model (GCM) TCs with observed landfalls using the International Best Track Archive for Climate Stewardship and reanalysis storm tracks. Using the SDD TC model described in Lin et al. (2023), we create a parallel set of tracks with HighResMIP daily kinematic and monthly thermodynamic fields as forcings from the same climate simulations. We find that downscaling produces a large sample size of storms in a computationally inexpensive manner but may introduce unphysical behaviors not observed in GCM TCs. Downscaling results in more uniform behavior across models, and there is evidence that some model biases may be inherited. SDD TC climatologies are more sensitive to the choice of model forcing than to the grid spacing of the model forcing. While each technique has distinct advantages and disadvantages, comparing them provides insights into the biases inherent in HighResMIP TC climatology. Diagnostics from the SDD runs reveal that the mechanisms underlying biases in TC climatology vary among HighResMIP models. An increased understanding of the strengths of these techniques is crucial for enhancing confidence in the results of future studies.